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About me

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** Published:**

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

** Published:**

Short description of portfolio item number 1

** Published:**

Short description of portfolio item number 2

Published in *ACTA PHYSICA POLONICA A*, 2020

R. Jankowski, J. Sienkiewicz, Acta Phys Pol A 138(1), 41-47 (2020), doi: 10.12693/APhysPolA.138.41 __http://przyrbwn.icm.edu.pl/APP/PDF/138/app138z1p06.pdf__

Published in *Entropy*, 2022

Jankowski, R.; Chmiel, A. Role of Time Scales in the Coupled Epidemic-Opinion Dynamics on Multiplex Networks. Entropy 2022, 24, 105. https://doi.org/10.3390/e24010105 __https://www.mdpi.com/1099-4300/24/1/105__

Published in *Nature Communications*, 2023

Jankowski, R., Allard, A., Boguñá, M. & Serrano, M. The D-Mercator method for the multidimensional hyperbolic embedding of real networks. Nat Commun 14, 7585 (2023). __https://www.nature.com/articles/s41467-023-43337-5__

Published in *arxiv*, 2024

Jankowski, R., Hozhabrierdi, P., Boguñá, M., & Serrano, M. Á. (2024). Feature-aware ultra-low dimensional reduction of real networks. arXiv:abs/2401.09368. __https://www.nature.com/articles/s41467-023-43337-5__

** Published:**

My first oral presentation during FENS 2019 - 10th Polish Symposium on Physics in Economy and Social Sciences. I talked about science of science :). Mainly, the usage of machine learning algorithms for application in bibliometrics. Which paper should we call popular? Is there any popularity threshold (i.e. the number of views)?

*Many thanks to my supervisor Dr Julian Sienkiewicz.*

More information in the presentation.

** Published:**

I proposed modified ADN model where as the input to the model we use the input signed network.

A figure presents our approach to leverage signed network as an input to the ADN model. The procedure yields both activity and preference distributions from the signed input network for modified ADN model. We begin with (1) finding exponent for $P(k_{out})$ distribution of signed network, which is used in the next step (2) to generate activity distribution. Analogously, the exponent in preference distribution is obtained from $P(k_{in})$ distribution (3). Then we generate agents’ preferences (4), which go into rate equation (5). The step in orange is optional (2*). It correlates the activity with the out-degree of the input signed network, i.e. the agent with the highest out-degree will generate more links. The last step (6) constitutes the final rate calculation, where we also utilize the input signed network.

More information in the presentation (in Polish).

** Published:**

Modelling the epidemic’s spread on multiplex networks, considering complex human behaviours, has recently gained the attention of many scientists. In this work, we study the interplay between epidemic spreading and opinion dynamics on multiplex networks. An agent in the epidemic layer could remain in one of five distinct states, resulting in the SIRQD model. The agent’s attitude towards respecting the restrictions of the pandemic plays a crucial role in its prevalence. In our model, the agent’s point of view could be altered by either conformism mechanism, social pressure, or independent actions. As the underlying opinion model, we leverage the q-voter model. The entire system constitutes a coupled opinion–dynamic model where two distinct processes occur. The question arises of how to properly align these dynamics, i.e., whether they should possess equal or disparate timescales. This paper highlights the impact of different timescales of opinion dynamics on epidemic spreading, focusing on the time and the infection’s peak.

More information in the presentation.

** Published:**

In this work we address that problem by introducing a method to map real complex networks into multidimensional hyperbolic spaces. The framework assumes that the structure of complex networks is well described by the $\mathbb{S}^D / \mathbb{H}^{D+1}$ model, i.e., the extension of well-known $\mathbb{S}^1/\mathbb{H}^2$ model into higher dimensions that has been proven to meaningfully explain properties of the real complex networks. Our method consists of two main parts. First, we leverage machine learning approach to infer the initial positions of the nodes on the $D$-sphere. Then by applying maximization likelihood technique we find the best matching between the observed network topology and the geometric model. Overall, our results suggest that mixing machine learning and ML techniques in a model-dependent framework can boost the meaningful mapping of complex networks.

Slides are available here.

Undergraduate course, *University 1, Department*, 2014

This is a description of a teaching experience. You can use markdown like any other post.